<<<<<<< HEAD Reporte estadístico

Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations822
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory183.2 KiB
Average record size in memory228.2 B

Variable types

Categorical5
Numeric11

Dataset

DescriptionEste es un analisis preeliminar para comprender de mejor forma los datos de nuestro dataset
AuthorKenneth David Leonel Triana , Juan Jose Naranjo, Alejandro Mora
URLhttps://github.com/kennethLeonel/Monografia-calidad-del-aire-valle-de-aburra

Alerts

anio has constant value "2024" Constant
festivo is highly imbalanced (72.5%) Imbalance
pm25 is highly skewed (γ1 = 28.62792907) Skewed
codigoserial is uniformly distributed Uniform
dia_semana is uniformly distributed Uniform
estacion is uniformly distributed Uniform
presion has 225 (27.4%) zeros Zeros
p1 has 213 (25.9%) zeros Zeros

Reproduction

Analysis started2024-10-16 21:17:35.613046
Analysis finished2024-10-16 21:17:46.142169
Duration10.53 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

anio
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
2024
822 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3288
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 822
100.0%

Length

2024-10-16T16:17:46.237742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations822
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory196.1 KiB
Average record size in memory244.2 B

Variable types

Categorical6
Numeric10

Dataset

DescriptionEste es un analisis preeliminar para comprender de mejor forma los datos de nuestro dataset
AuthorKenneth David Leonel Triana , Juan Jose Naranjo, Alejandro Mora
URLhttps://github.com/kennethLeonel/Monografia-calidad-del-aire-valle-de-aburra

Alerts

anio has constant value "2024"Constant
festivo is highly imbalanced (72.5%)Imbalance
p1 is highly imbalanced (52.0%)Imbalance
codigoserial is uniformly distributedUniform
dia_semana is uniformly distributedUniform
estacion is uniformly distributedUniform
presion has 242 (29.4%) zerosZeros

Reproduction

Analysis started2024-10-16 23:49:41.410582
Analysis finished2024-10-16 23:50:11.715107
Duration30.3 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

anio
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.4 KiB
2024
822 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3288
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 822
100.0%

Length

2024-10-16T18:50:11.919591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T16:17:46.332845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T18:50:12.168947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2024 822
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

mes
Real number (ℝ)

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0072993
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2024-10-16T16:17:46.407636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2024 822
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

mes
Real number (ℝ)

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0072993
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2024-10-16T18:50:12.409633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5816641
Coefficient of variation (CV)0.51558014
Kurtosis-1.2270774
Mean5.0072993
Median Absolute Deviation (MAD)2
Skewness-0.010631573
Sum4116
Variance6.6649893
MonotonicityNot monotonic
2024-10-16T18:50:12.652952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5816641
Coefficient of variation (CV)0.51558014
Kurtosis-1.2270774
Mean5.0072993
Median Absolute Deviation (MAD)2
Skewness-0.010631573
Sum4116
Variance6.6649893
MonotonicityNot monotonic
2024-10-16T16:17:46.497193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 93
11.3%
3 93
11.3%
5 93
11.3%
7 93
11.3%
8 93
11.3%
4 90
10.9%
6 90
10.9%
9 90
10.9%
2 87
10.6%
ValueCountFrequency (%)
1 93
11.3%
2 87
10.6%
3 93
11.3%
4 90
10.9%
5 93
11.3%
6 90
10.9%
7 93
11.3%
8 93
11.3%
9 90
10.9%
ValueCountFrequency (%)
9 90
10.9%
8 93
11.3%
7 93
11.3%
6 90
10.9%
5 93
11.3%
4 90
10.9%
3 93
11.3%
2 87
10.6%
1 93
11.3%

dia
Real number (ℝ)

Distinct31
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729927
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2024-10-16T18:50:12.896978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8023391
Coefficient of variation (CV)0.55959186
Kurtosis-1.1958321
Mean15.729927
Median Absolute Deviation (MAD)8
Skewness0.0050555266
Sum12930
Variance77.481174
MonotonicityNot monotonic
2024-10-16T18:50:13.112681image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 93
11.3%
3 93
11.3%
5 93
11.3%
7 93
11.3%
8 93
11.3%
4 90
10.9%
6 90
10.9%
9 90
10.9%
2 87
10.6%
ValueCountFrequency (%)
1 93
11.3%
2 87
10.6%
3 93
11.3%
4 90
10.9%
5 93
11.3%
6 90
10.9%
7 93
11.3%
8 93
11.3%
9 90
10.9%
ValueCountFrequency (%)
9 90
10.9%
8 93
11.3%
7 93
11.3%
6 90
10.9%
5 93
11.3%
4 90
10.9%
3 93
11.3%
2 87
10.6%
1 93
11.3%

dia
Real number (ℝ)

Distinct31
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729927
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2024-10-16T16:17:46.592253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 27
 
3.3%
2 27
 
3.3%
29 27
 
3.3%
28 27
 
3.3%
27 27
 
3.3%
26 27
 
3.3%
25 27
 
3.3%
24 27
 
3.3%
23 27
 
3.3%
22 27
 
3.3%
Other values (21) 552
67.2%
ValueCountFrequency (%)
1 27
3.3%
2 27
3.3%
3 27
3.3%
4 27
3.3%
5 27
3.3%
6 27
3.3%
7 27
3.3%
8 27
3.3%
9 27
3.3%
10 27
3.3%
ValueCountFrequency (%)
31 15
1.8%
30 24
2.9%
29 27
3.3%
28 27
3.3%
27 27
3.3%
26 27
3.3%
25 27
3.3%
24 27
3.3%
23 27
3.3%
22 27
3.3%

pm25
Real number (ℝ)

Distinct608
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.97324
Minimum-9999
Maximum99999
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)0.5%
Memory size12.8 KiB
2024-10-16T18:50:13.361118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8023391
Coefficient of variation (CV)0.55959186
Kurtosis-1.1958321
Mean15.729927
Median Absolute Deviation (MAD)8
Skewness0.0050555266
Sum12930
Variance77.481174
MonotonicityNot monotonic
2024-10-16T16:17:46.690321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 27
 
3.3%
2 27
 
3.3%
29 27
 
3.3%
28 27
 
3.3%
27 27
 
3.3%
26 27
 
3.3%
25 27
 
3.3%
24 27
 
3.3%
23 27
 
3.3%
22 27
 
3.3%
Other values (21) 552
67.2%
ValueCountFrequency (%)
1 27
3.3%
2 27
3.3%
3 27
3.3%
4 27
3.3%
5 27
3.3%
6 27
3.3%
7 27
3.3%
8 27
3.3%
9 27
3.3%
10 27
3.3%
ValueCountFrequency (%)
31 15
1.8%
30 24
2.9%
29 27
3.3%
28 27
3.3%
27 27
3.3%
26 27
3.3%
25 27
3.3%
24 27
3.3%
23 27
3.3%
22 27
3.3%

pm25
Real number (ℝ)

Skewed 

Distinct778
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5655.2644
Minimum-9999
Maximum4216635.3
Zeros0
Zeros (%)0.0%
Negative63
Negative (%)7.7%
Memory size12.8 KiB
2024-10-16T16:17:46.795421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile-405.4405
Q114.321539
median19.186874
Q326.958333
95-th percentile4179.338
Maximum4216635.3
Range4226634.3
Interquartile range (IQR)12.636794

Descriptive statistics

Standard deviation147127.23
Coefficient of variation (CV)26.015978
Kurtosis820.3519
Mean5655.2644
Median Absolute Deviation (MAD)5.7285406
Skewness28.627929
Sum4648627.4
Variance2.1646423 × 1010
MonotonicityNot monotonic
2024-10-16T16:17:46.902595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile10.53648
Q114.391738
median18.408425
Q324.935037
95-th percentile37.611642
Maximum99999
Range109998
Interquartile range (IQR)10.5433

Descriptive statistics

Standard deviation4980.0982
Coefficient of variation (CV)23.166131
Kurtosis392.58198
Mean214.97324
Median Absolute Deviation (MAD)4.747375
Skewness19.601092
Sum176708.01
Variance24801378
MonotonicityNot monotonic
2024-10-16T18:50:14.013345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.5 3
 
0.4%
13.45833333 3
 
0.4%
18.08333333 3
 
0.4%
16.625 3
 
0.4%
19.70833333 3
 
0.4%
17.45833333 3
 
0.4%
15.54166667 2
 
0.2%
20.41666667 2
 
0.2%
28.875 2
 
0.2%
13.25 2
 
0.2%
Other values (768) 796
96.8%
ValueCountFrequency (%)
-9999 1
0.1%
-5823.583333 1
0.1%
-5407.477219 1
0.1%
-4575.428069 1
0.1%
-4160.207614 1
0.1%
-4155.650887 1
0.1%
-4153.013067 1
0.1%
-2487.168203 1
0.1%
-2480.750017 1
0.1%
-2067.635837 1
0.1%
ValueCountFrequency (%)
4216635.281 1
0.1%
99999 1
0.1%
45840.1535 1
0.1%
41671.96746 1
0.1%
32093.08017 1
0.1%
20846.47932 1
0.1%
16692.34784 1
0.1%
16679.97371 1
0.1%
16677.98897 1
0.1%
16677.89734 1
0.1%

codigoserial
Categorical

Uniform 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size47.4 KiB
28
274 
69
274 
86
274 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1644
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28
2nd row28
3rd row28
4th row28
5th row28

Common Values

ValueCountFrequency (%)
28 274
33.3%
69 274
33.3%
86 274
33.3%

Length

2024-10-16T16:17:46.996193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 14
 
1.7%
19 11
 
1.3%
16.5 11
 
1.3%
18.5 10
 
1.2%
15 9
 
1.1%
18 9
 
1.1%
13.5 8
 
1.0%
20 8
 
1.0%
17.5 7
 
0.9%
14 7
 
0.9%
Other values (598) 728
88.6%
ValueCountFrequency (%)
-9999 4
0.5%
1 3
0.4%
5.36807 1
 
0.1%
5.56998 1
 
0.1%
6.12773 1
 
0.1%
6.662345 1
 
0.1%
6.685655 1
 
0.1%
7.309475 1
 
0.1%
7.5 2
0.2%
7.85481 1
 
0.1%
ValueCountFrequency (%)
99999 2
0.2%
49 1
0.1%
46.81495 1
0.1%
46.5 1
0.1%
46.2742 1
0.1%
45.8667 1
0.1%
45.5 1
0.1%
45 1
0.1%
44.8498 1
0.1%
44.8023 1
0.1%

codigoserial
Categorical

UNIFORM 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
28
274 
69
274 
86
274 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1644
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28
2nd row28
3rd row28
4th row28
5th row28

Common Values

ValueCountFrequency (%)
28 274
33.3%
69 274
33.3%
86 274
33.3%

Length

2024-10-16T18:50:14.241304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T16:17:47.079543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T18:50:14.450006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
28 274
33.3%
69 274
33.3%
86 274
33.3%

Most occurring characters

ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

dia_semana
Categorical

Uniform 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
Lunes
120 
Martes
117 
Miercoles
117 
Jueves
117 
Viernes
117 
Other values (2)
234 

Length

Max length9
Median length7
Mean length6.5656934
Min length5

Characters and Unicode

Total characters5397
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLunes
2nd rowMartes
3rd rowMiercoles
4th rowJueves
5th rowViernes

Common Values

ValueCountFrequency (%)
Lunes 120
14.6%
Martes 117
14.2%
Miercoles 117
14.2%
Jueves 117
14.2%
Viernes 117
14.2%
Sabado 117
14.2%
Domingo 117
14.2%

Length

2024-10-16T16:17:47.183984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
28 274
33.3%
69 274
33.3%
86 274
33.3%

Most occurring characters

ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

dia_semana
Categorical

UNIFORM 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
Lunes
120 
Martes
117 
Miercoles
117 
Jueves
117 
Viernes
117 
Other values (2)
234 

Length

Max length9
Median length7
Mean length6.5656934
Min length5

Characters and Unicode

Total characters5397
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLunes
2nd rowMartes
3rd rowMiercoles
4th rowJueves
5th rowViernes

Common Values

ValueCountFrequency (%)
Lunes 120
14.6%
Martes 117
14.2%
Miercoles 117
14.2%
Jueves 117
14.2%
Viernes 117
14.2%
Sabado 117
14.2%
Domingo 117
14.2%

Length

2024-10-16T18:50:14.725057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T16:17:47.296537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T18:50:14.988051image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
lunes 120
14.6%
martes 117
14.2%
miercoles 117
14.2%
jueves 117
14.2%
viernes 117
14.2%
sabado 117
14.2%
domingo 117
14.2%

Most occurring characters

ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

estacion
Categorical

Uniform 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size58.3 KiB
Estacion Itagui
274 
Estacion Caldas
274 
Estacion Aranjuez
274 

Length

Max length17
Median length15
Mean length15.666667
Min length15

Characters and Unicode

Total characters12878
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstacion Itagui
2nd rowEstacion Itagui
3rd rowEstacion Itagui
4th rowEstacion Itagui
5th rowEstacion Itagui

Common Values

ValueCountFrequency (%)
Estacion Itagui 274
33.3%
Estacion Caldas 274
33.3%
Estacion Aranjuez 274
33.3%

Length

2024-10-16T16:17:47.638811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
lunes 120
14.6%
martes 117
14.2%
miercoles 117
14.2%
jueves 117
14.2%
viernes 117
14.2%
sabado 117
14.2%
domingo 117
14.2%

Most occurring characters

ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

estacion
Categorical

UNIFORM 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size64.8 KiB
Estacion Itagui
274 
Estacion Caldas
274 
Estacion Aranjuez
274 

Length

Max length17
Median length15
Mean length15.666667
Min length15

Characters and Unicode

Total characters12878
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstacion Itagui
2nd rowEstacion Itagui
3rd rowEstacion Itagui
4th rowEstacion Itagui
5th rowEstacion Itagui

Common Values

ValueCountFrequency (%)
Estacion Itagui 274
33.3%
Estacion Caldas 274
33.3%
Estacion Aranjuez 274
33.3%

Length

2024-10-16T18:50:15.285772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T16:17:47.728985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T18:50:15.509280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
estacion 822
50.0%
itagui 274
 
16.7%
caldas 274
 
16.7%
aranjuez 274
 
16.7%

Most occurring characters

ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

festivo
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size46.6 KiB
0
783 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters822
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Length

2024-10-16T16:17:47.824449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
estacion 822
50.0%
itagui 274
 
16.7%
caldas 274
 
16.7%
aranjuez 274
 
16.7%

Most occurring characters

ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

festivo
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size53.0 KiB
0
783 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters822
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Length

2024-10-16T18:50:15.723876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T16:17:47.900603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T18:50:15.899671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

temperatura
Real number (ℝ)

Distinct748
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.234121
Minimum-999
Maximum25.805556
Zeros0
Zeros (%)0.0%
Negative88
Negative (%)10.7%
Memory size12.8 KiB
2024-10-16T16:17:47.992295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

temperatura
Real number (ℝ)

Distinct219
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.148966
Minimum-999
Maximum25.5
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T18:50:16.109688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q119.824358
median21.754094
Q323.441821
95-th percentile24.64388
Maximum25.805556
Range1024.8056
Interquartile range (IQR)3.6174635

Descriptive statistics

Standard deviation292.08232
Coefficient of variation (CV)-3.9346101
Kurtosis5.9065192
Mean-74.234121
Median Absolute Deviation (MAD)1.7726007
Skewness-2.7890436
Sum-61020.447
Variance85312.081
MonotonicityNot monotonic
2024-10-16T16:17:48.103666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q119.5
median21.3
Q322.9
95-th percentile24.195
Maximum25.5
Range1024.5
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation297.51698
Coefficient of variation (CV)-4.0124225
Kurtosis5.8206594
Mean-74.148966
Median Absolute Deviation (MAD)1.6975
Skewness-2.7939302
Sum-60950.45
Variance88516.354
MonotonicityNot monotonic
2024-10-16T18:50:16.392202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
21 20
 
2.4%
23.5 16
 
1.9%
22.4 16
 
1.9%
23.4 15
 
1.8%
23 15
 
1.8%
20.1 14
 
1.7%
22.5 14
 
1.7%
22.9 14
 
1.7%
21.9 14
 
1.7%
Other values (209) 607
73.8%
ValueCountFrequency (%)
-999 77
9.4%
16.1 1
 
0.1%
16.5 1
 
0.1%
16.7 1
 
0.1%
16.8 3
 
0.4%
16.9 2
 
0.2%
17 5
 
0.6%
17.1 1
 
0.1%
17.2 4
 
0.5%
17.3 4
 
0.5%
ValueCountFrequency (%)
25.5 1
 
0.1%
25.375 1
 
0.1%
25.1 1
 
0.1%
25.09 1
 
0.1%
25 1
 
0.1%
24.995 1
 
0.1%
24.9 1
 
0.1%
24.85 1
 
0.1%
24.8 4
0.5%
24.799999 1
 
0.1%

humedad
Real number (ℝ)

Distinct426
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-25.815931
Minimum-999
Maximum91.8
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T18:50:16.651896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q165.8125
median74.05
Q381.15
95-th percentile86.1
Maximum91.8
Range1090.8
Interquartile range (IQR)15.3375

Descriptive statistics

Standard deviation313.16084
Coefficient of variation (CV)-12.130527
Kurtosis5.8102623
Mean-25.815931
Median Absolute Deviation (MAD)7.55
Skewness-2.7907922
Sum-21220.695
Variance98069.709
MonotonicityNot monotonic
2024-10-16T18:50:16.918593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
81 14
 
1.7%
78 12
 
1.5%
73 12
 
1.5%
82 12
 
1.5%
76 12
 
1.5%
85 11
 
1.3%
75 10
 
1.2%
84 10
 
1.2%
80 10
 
1.2%
Other values (416) 642
78.1%
ValueCountFrequency (%)
-999 77
9.4%
50.2 1
 
0.1%
51.25 1
 
0.1%
52.15 1
 
0.1%
52.15 1
 
0.1%
52.5 2
 
0.2%
52.9 1
 
0.1%
54 1
 
0.1%
54.2 1
 
0.1%
54.95 1
 
0.1%
ValueCountFrequency (%)
91.8 1
 
0.1%
91.43 1
 
0.1%
91 1
 
0.1%
90.9 1
 
0.1%
89.5 1
 
0.1%
89 3
0.4%
88.8 1
 
0.1%
88.68 1
 
0.1%
88.55 1
 
0.1%
88.5 1
 
0.1%

presion
Real number (ℝ)

ZEROS 

Distinct95
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.48345
Minimum-999
Maximum854.6
Zeros242
Zeros (%)29.4%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T18:50:17.172281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10
median826
Q3851.5
95-th percentile853
Maximum854.6
Range1853.6
Interquartile range (IQR)851.5

Descriptive statistics

Standard deviation590.81487
Coefficient of variation (CV)1.4050847
Kurtosis0.37110377
Mean420.48345
Median Absolute Deviation (MAD)26.5
Skewness-1.1897845
Sum345637.4
Variance349062.21
MonotonicityNot monotonic
2024-10-16T18:50:17.444370image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 70
 
8.5%
19.81208333 2
 
0.2%
18.25715278 2
 
0.2%
22.61555556 2
 
0.2%
23.85430556 2
 
0.2%
21.50506944 2
 
0.2%
19.76848613 1
 
0.1%
24.60888889 1
 
0.1%
23.99152778 1
 
0.1%
24.12041667 1
 
0.1%
Other values (738) 738
89.8%
ValueCountFrequency (%)
-999 70
8.5%
-973.3485417 1
 
0.1%
-967.7763889 1
 
0.1%
-583.7022014 1
 
0.1%
-577.5151389 1
 
0.1%
-577.2209722 1
 
0.1%
-494.8014722 1
 
0.1%
-490.0909028 1
 
0.1%
-487.1159722 1
 
0.1%
-486.8761111 1
 
0.1%
ValueCountFrequency (%)
25.80555556 1
0.1%
25.60798611 1
0.1%
25.60576389 1
0.1%
25.52388889 1
0.1%
25.51756944 1
0.1%
25.50707636 1
0.1%
25.49993056 1
0.1%
25.49090278 1
0.1%
25.47887501 1
0.1%
25.42810416 1
0.1%

humedad
Real number (ℝ)

Distinct753
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-29.625042
Minimum-999
Maximum88.604813
Zeros0
Zeros (%)0.0%
Negative84
Negative (%)10.2%
Memory size12.8 KiB
2024-10-16T16:17:48.209265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q161.80849
median70.541875
Q376.706493
95-th percentile83.036926
Maximum88.604813
Range1087.6048
Interquartile range (IQR)14.898003

Descriptive statistics

Standard deviation306.21792
Coefficient of variation (CV)-10.336455
Kurtosis5.9010952
Mean-29.625042
Median Absolute Deviation (MAD)7.0651042
Skewness-2.7869321
Sum-24351.784
Variance93769.417
MonotonicityNot monotonic
2024-10-16T16:17:48.317878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 242
29.4%
-999 77
 
9.4%
825.8 17
 
2.1%
826.7 17
 
2.1%
852.4 16
 
1.9%
852.3 15
 
1.8%
852.1 15
 
1.8%
826.5 14
 
1.7%
852.9 13
 
1.6%
852 13
 
1.6%
Other values (85) 383
46.6%
ValueCountFrequency (%)
-999 77
 
9.4%
0 242
29.4%
823.5 1
 
0.1%
824.1 1
 
0.1%
824.2 1
 
0.1%
824.3 2
 
0.2%
824.4 1
 
0.1%
824.6 3
 
0.4%
824.7 4
 
0.5%
824.8 5
 
0.6%
ValueCountFrequency (%)
854.6 1
 
0.1%
854.1 2
0.2%
854 1
 
0.1%
853.9 4
0.5%
853.85 1
 
0.1%
853.8 2
0.2%
853.7 2
0.2%
853.6 2
0.2%
853.55 1
 
0.1%
853.5 2
0.2%

p1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
0.0
737 
-999.0
85 

Length

Max length6
Median length3
Mean length3.310219
Min length3

Characters and Unicode

Total characters2721
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 737
89.7%
-999.0 85
 
10.3%

Length

2024-10-16T18:50:17.701956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T18:50:17.914778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 737
89.7%
999.0 85
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 1559
57.3%
. 822
30.2%
9 255
 
9.4%
- 85
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1559
57.3%
. 822
30.2%
9 255
 
9.4%
- 85
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1559
57.3%
. 822
30.2%
9 255
 
9.4%
- 85
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1559
57.3%
. 822
30.2%
9 255
 
9.4%
- 85
 
3.1%

velocidad_prom
Real number (ℝ)

Distinct163
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-92.201259
Minimum-999
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T18:50:18.146379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q11.11125
median1.415
Q31.7875
95-th percentile2.28
Maximum3.5
Range1002.5
Interquartile range (IQR)0.67625

Descriptive statistics

Standard deviation291.70438
Coefficient of variation (CV)-3.1637787
Kurtosis5.8212824
Mean-92.201259
Median Absolute Deviation (MAD)0.315
Skewness-2.7941186
Sum-75789.435
Variance85091.443
MonotonicityNot monotonic
2024-10-16T18:50:18.420611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
1.4 58
 
7.1%
1.5 58
 
7.1%
1.2 58
 
7.1%
1.6 47
 
5.7%
1.3 47
 
5.7%
1.8 33
 
4.0%
1.7 31
 
3.8%
1.1 25
 
3.0%
1 22
 
2.7%
Other values (153) 366
44.5%
ValueCountFrequency (%)
-999 77
9.4%
0.1 2
 
0.2%
0.2 1
 
0.1%
0.5 1
 
0.1%
0.6 9
 
1.1%
0.7 19
 
2.3%
0.8 18
 
2.2%
0.9 15
 
1.8%
0.93 1
 
0.1%
0.99 2
 
0.2%
ValueCountFrequency (%)
3.5 1
0.1%
3.3 1
0.1%
3.1 2
0.2%
3 2
0.2%
2.9 1
0.1%
2.7 2
0.2%
2.61 1
0.1%
2.6 2
0.2%
2.59 1
0.1%
2.5 1
0.1%

velocidad_max
Real number (ℝ)

Distinct57
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.358942
Minimum-999
Maximum5
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T18:50:18.678337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q11.8
median2.3
Q32.9
95-th percentile3.5
Maximum5
Range1004
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation291.97579
Coefficient of variation (CV)-3.1959191
Kurtosis5.8212299
Mean-91.358942
Median Absolute Deviation (MAD)0.6
Skewness-2.7941027
Sum-75097.05
Variance85249.86
MonotonicityNot monotonic
2024-10-16T18:50:18.948549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
1.9 47
 
5.7%
2.9 39
 
4.7%
2.2 37
 
4.5%
2 37
 
4.5%
1.8 37
 
4.5%
2.3 37
 
4.5%
1.7 36
 
4.4%
3.1 35
 
4.3%
2.8 35
 
4.3%
Other values (47) 405
49.3%
ValueCountFrequency (%)
-999 77
9.4%
0.3 1
 
0.1%
0.4 1
 
0.1%
0.55 1
 
0.1%
0.9 1
 
0.1%
1 3
 
0.4%
1.1 7
 
0.9%
1.2 8
 
1.0%
1.3 11
 
1.3%
1.4 13
 
1.6%
ValueCountFrequency (%)
5 1
 
0.1%
4.7 1
 
0.1%
4.5 2
 
0.2%
4.4 5
0.6%
4.2 1
 
0.1%
4.1 2
 
0.2%
4 4
0.5%
3.9 3
0.4%
3.85 1
 
0.1%
3.8 3
0.4%

direccion_prom
Real number (ℝ)

Distinct336
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.837591
Minimum-999
Maximum338
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T18:50:19.222711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q174.125
median130
Q3167
95-th percentile272
Maximum338
Range1337
Interquartile range (IQR)92.875

Descriptive statistics

Standard deviation338.53405
Coefficient of variation (CV)9.7174929
Kurtosis5.246014
Mean34.837591
Median Absolute Deviation (MAD)46
Skewness-2.6116487
Sum28636.5
Variance114605.3
MonotonicityNot monotonic
2024-10-16T18:50:19.474041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 70
 
8.5%
74.87365279 1
 
0.1%
65.64958333 1
 
0.1%
57.05555556 1
 
0.1%
57.02277778 1
 
0.1%
58.32020833 1
 
0.1%
61.95875 1
 
0.1%
56.85229167 1
 
0.1%
45.84041667 1
 
0.1%
50.73298611 1
 
0.1%
Other values (743) 743
90.4%
ValueCountFrequency (%)
-999 70
8.5%
-972.8702778 1
 
0.1%
-966.1264583 1
 
0.1%
-568.7318056 1
 
0.1%
-558.5639375 1
 
0.1%
-552.68375 1
 
0.1%
-468.0622222 1
 
0.1%
-464.3461389 1
 
0.1%
-457.6452083 1
 
0.1%
-456.7511111 1
 
0.1%
ValueCountFrequency (%)
88.60481251 1
0.1%
87.52608331 1
0.1%
87.07765277 1
0.1%
86.09479167 1
0.1%
85.533125 1
0.1%
85.32188193 1
0.1%
85.25628471 1
0.1%
85.25097222 1
0.1%
85.18998613 1
0.1%
85.01345833 1
0.1%

presion
Real number (ℝ)

Zeros 

Distinct523
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.34831
Minimum-999
Maximum854.00083
Zeros225
Zeros (%)27.4%
Negative97
Negative (%)11.8%
Memory size12.8 KiB
2024-10-16T16:17:48.424501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
135 14
 
1.7%
124 13
 
1.6%
138 12
 
1.5%
134 12
 
1.5%
126 12
 
1.5%
129 12
 
1.5%
131 10
 
1.2%
125 10
 
1.2%
130 9
 
1.1%
Other values (326) 641
78.0%
ValueCountFrequency (%)
-999 77
9.4%
0.5 1
 
0.1%
4.5 1
 
0.1%
29 1
 
0.1%
30 2
 
0.2%
32 1
 
0.1%
32.5 1
 
0.1%
33 2
 
0.2%
34 2
 
0.2%
35 1
 
0.1%
ValueCountFrequency (%)
338 1
 
0.1%
335.5 1
 
0.1%
327 3
0.4%
326 3
0.4%
322.5 1
 
0.1%
321.5 1
 
0.1%
320 1
 
0.1%
318 1
 
0.1%
315 1
 
0.1%
313.5 1
 
0.1%

direccion_max
Real number (ℝ)

Distinct302
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.058394
Minimum-999
Maximum333
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T18:50:19.711019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10
median825.66128
Q3850.78964
95-th percentile852.63293
Maximum854.00083
Range1853.0008
Interquartile range (IQR)850.78964

Descriptive statistics

Standard deviation582.67401
Coefficient of variation (CV)1.3927964
Kurtosis0.34209453
Mean418.34831
Median Absolute Deviation (MAD)26.603333
Skewness-1.1601691
Sum343882.31
Variance339509
MonotonicityNot monotonic
2024-10-16T16:17:48.533104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 225
27.4%
-999 70
 
8.5%
-1.3875 3
 
0.4%
-0.69375 3
 
0.4%
-2.08125 2
 
0.2%
-2.775 2
 
0.2%
-942.4175694 1
 
0.1%
850.4498611 1
 
0.1%
-77.69125 1
 
0.1%
-952.7500694 1
 
0.1%
Other values (513) 513
62.4%
ValueCountFrequency (%)
-999 70
8.5%
-952.7500694 1
 
0.1%
-942.4175694 1
 
0.1%
-592.4625 1
 
0.1%
-505.74375 1
 
0.1%
-244.2071528 1
 
0.1%
-238.9220139 1
 
0.1%
-81.81826389 1
 
0.1%
-80.75256944 1
 
0.1%
-77.69125 1
 
0.1%
ValueCountFrequency (%)
854.0008333 1
0.1%
853.8609028 1
0.1%
853.7610417 1
0.1%
853.5519444 1
0.1%
853.4649306 1
0.1%
853.2995139 1
0.1%
853.2894444 1
0.1%
853.2795833 1
0.1%
853.2552778 1
0.1%
853.2316667 1
0.1%

p1
Real number (ℝ)

Zeros 

Distinct275
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-107.18585
Minimum-999
Maximum0.064527778
Zeros213
Zeros (%)25.9%
Negative161
Negative (%)19.6%
Memory size12.8 KiB
2024-10-16T16:17:48.645377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q171.25
median141
Q3187
95-th percentile264
Maximum333
Range1332
Interquartile range (IQR)115.75

Descriptive statistics

Standard deviation340.14222
Coefficient of variation (CV)8.4911596
Kurtosis5.2506013
Mean40.058394
Median Absolute Deviation (MAD)58
Skewness-2.6174331
Sum32928
Variance115696.73
MonotonicityNot monotonic
2024-10-16T18:50:19.963705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10
median6.9444444 × 10-6
Q30.0033055556
95-th percentile0.023081944
Maximum0.064527778
Range999.06453
Interquartile range (IQR)0.0033055556

Descriptive statistics

Standard deviation301.37977
Coefficient of variation (CV)-2.8117497
Kurtosis4.6492889
Mean-107.18585
Median Absolute Deviation (MAD)0.0016944444
Skewness-2.5524066
Sum-88106.766
Variance90829.766
MonotonicityNot monotonic
2024-10-16T16:17:48.757019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 213
25.9%
-999 79
 
9.6%
6.944444444 × 10-639
 
4.7%
1.388888889 × 10-522
 
2.7%
2.083333333 × 10-514
 
1.7%
-0.69375 11
 
1.3%
2.777777778 × 10-58
 
1.0%
7.638888889 × 10-55
 
0.6%
4.861111111 × 10-55
 
0.6%
4.166666667 × 10-55
 
0.6%
Other values (265) 421
51.2%
ValueCountFrequency (%)
-999 79
9.6%
-974.025 1
 
0.1%
-968.475 1
 
0.1%
-588.3 1
 
0.1%
-585.525 2
 
0.2%
-501.58125 1
 
0.1%
-496.725 2
 
0.2%
-496.7211181 2
 
0.2%
-482.1391806 2
 
0.2%
-359.3587917 2
 
0.2%
ValueCountFrequency (%)
0.06452777778 2
0.2%
0.05119444444 2
0.2%
0.04704166667 1
0.1%
0.04621527778 2
0.2%
0.04209027778 2
0.2%
0.03425694444 1
0.1%
0.03263888889 2
0.2%
0.03171527778 2
0.2%
0.03158333333 2
0.2%
0.03149305556 1
0.1%

velocidad_prom
Real number (ℝ)

Distinct747
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-92.735
Minimum-999
Maximum3.4436111
Zeros0
Zeros (%)0.0%
Negative121
Negative (%)14.7%
Memory size12.8 KiB
2024-10-16T16:17:48.865156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q11.1687326
median1.5713194
Q31.8784826
95-th percentile2.4087514
Maximum3.4436111
Range1002.4436
Interquartile range (IQR)0.70975

Descriptive statistics

Standard deviation286.22487
Coefficient of variation (CV)-3.0864815
Kurtosis5.9081556
Mean-92.735
Median Absolute Deviation (MAD)0.33965278
Skewness-2.789423
Sum-76228.17
Variance81924.674
MonotonicityNot monotonic
2024-10-16T16:17:48.976385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 70
 
8.5%
1.664861111 2
 
0.2%
1.575694444 2
 
0.2%
1.575347222 2
 
0.2%
1.612013889 2
 
0.2%
1.345277778 2
 
0.2%
1.776805556 2
 
0.2%
0.8103472222 1
 
0.1%
1.363194444 1
 
0.1%
1.641388889 1
 
0.1%
Other values (737) 737
89.7%
ValueCountFrequency (%)
-999 70
8.5%
-973.9296528 1
 
0.1%
-968.4334722 1
 
0.1%
-591.8551181 1
 
0.1%
-587.5753472 1
 
0.1%
-584.7041667 1
 
0.1%
-504.8587361 1
 
0.1%
-500.8329167 1
 
0.1%
-496.1823611 1
 
0.1%
-495.9430556 1
 
0.1%
ValueCountFrequency (%)
3.443611111 1
0.1%
3.122361111 1
0.1%
3.081736111 1
0.1%
3.025694444 1
0.1%
2.898194444 1
0.1%
2.753708333 1
0.1%
2.744375 1
0.1%
2.74 1
0.1%
2.687604167 1
0.1%
2.671770833 1
0.1%

velocidad_max
Real number (ℝ)

Distinct747
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.80445
Minimum-999
Maximum4.9930556
Zeros0
Zeros (%)0.0%
Negative108
Negative (%)13.1%
Memory size12.8 KiB
2024-10-16T16:17:49.083810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q11.9463889
median2.5347917
Q33.0812153
95-th percentile3.7179167
Maximum4.9930556
Range1003.9931
Interquartile range (IQR)1.1348264

Descriptive statistics

Standard deviation286.52051
Coefficient of variation (CV)-3.1209872
Kurtosis5.9079516
Mean-91.80445
Median Absolute Deviation (MAD)0.56944444
Skewness-2.7893823
Sum-75463.258
Variance82094.004
MonotonicityNot monotonic
2024-10-16T16:17:49.197644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 70
 
8.5%
2.00875 2
 
0.2%
2.903611111 2
 
0.2%
2.44125 2
 
0.2%
2.440069444 2
 
0.2%
2.092777778 2
 
0.2%
2.534444444 2
 
0.2%
0.5947222222 1
 
0.1%
4.208819444 1
 
0.1%
2.134236111 1
 
0.1%
Other values (737) 737
89.7%
ValueCountFrequency (%)
-999 70
8.5%
-973.8575694 1
 
0.1%
-968.3997917 1
 
0.1%
-591.5904167 1
 
0.1%
-586.9434722 1
 
0.1%
-584.3690972 1
 
0.1%
-504.4868056 1
 
0.1%
-500.2472917 1
 
0.1%
-495.9194444 1
 
0.1%
-495.5963194 1
 
0.1%
ValueCountFrequency (%)
4.993055556 1
0.1%
4.681458333 1
0.1%
4.515555556 1
0.1%
4.511180556 1
0.1%
4.412986111 1
0.1%
4.349027778 1
0.1%
4.208819444 1
0.1%
4.18 1
0.1%
4.162361111 1
0.1%
4.134583333 1
0.1%

direccion_prom
Real number (ℝ)

Distinct750
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.11877
Minimum-999
Maximum255.27292
Zeros0
Zeros (%)0.0%
Negative83
Negative (%)10.1%
Memory size12.8 KiB
2024-10-16T16:17:49.303576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1131.46059
median155.8309
Q3180.85521
95-th percentile208.71969
Maximum255.27292
Range1254.2729
Interquartile range (IQR)49.394618

Descriptive statistics

Standard deviation333.19348
Coefficient of variation (CV)6.518026
Kurtosis5.7516313
Mean51.11877
Median Absolute Deviation (MAD)25.000347
Skewness-2.7426624
Sum42019.629
Variance111017.89
MonotonicityNot monotonic
2024-10-16T16:17:49.413809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 70
 
8.5%
168.2680556 2
 
0.2%
142.3944444 2
 
0.2%
193.1611111 2
 
0.2%
208.6076389 1
 
0.1%
146.9715278 1
 
0.1%
168.6756944 1
 
0.1%
199.6645833 1
 
0.1%
137.7208333 1
 
0.1%
139.5618056 1
 
0.1%
Other values (740) 740
90.0%
ValueCountFrequency (%)
-999 70
8.5%
-971.9159722 1
 
0.1%
-961.1381944 1
 
0.1%
-541.8020833 1
 
0.1%
-537.6381944 1
 
0.1%
-494.4652778 1
 
0.1%
-439.9194444 1
 
0.1%
-432.6798611 1
 
0.1%
-419.5256944 1
 
0.1%
-405.7173611 1
 
0.1%
ValueCountFrequency (%)
255.2729167 1
0.1%
243.8215278 1
0.1%
237.8340278 1
0.1%
237.2402778 1
0.1%
236.2916667 1
0.1%
235.7381944 1
0.1%
234.4993056 1
0.1%
229.8847222 1
0.1%
229.4333333 1
0.1%
229.2451389 1
0.1%

direccion_max
Real number (ℝ)

Distinct748
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.468737
Minimum-999
Maximum247.76875
Zeros0
Zeros (%)0.0%
Negative83
Negative (%)10.1%
Memory size12.8 KiB
2024-10-16T16:17:49.522358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1138.83524
median164.30833
Q3184.31927
95-th percentile207.90378
Maximum247.76875
Range1246.7687
Interquartile range (IQR)45.484028

Descriptive statistics

Standard deviation334.35008
Coefficient of variation (CV)6.027721
Kurtosis5.7743245
Mean55.468737
Median Absolute Deviation (MAD)21.998958
Skewness-2.7498711
Sum45595.302
Variance111789.97
MonotonicityNot monotonic
2024-10-16T16:17:49.638045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 70
 
8.5%
190.1854167 2
 
0.2%
166.6222222 2
 
0.2%
190.9041667 2
 
0.2%
175.7930556 2
 
0.2%
165.4381944 2
 
0.2%
160.3826389 1
 
0.1%
123.975 1
 
0.1%
159.3604167 1
 
0.1%
102.9256944 1
 
0.1%
Other values (738) 738
89.8%
ValueCountFrequency (%)
-999 70
8.5%
-971.8826389 1
 
0.1%
-962.6 1
 
0.1%
-538.9166667 1
 
0.1%
-535.5097222 1
 
0.1%
-493.7798611 1
 
0.1%
-436.2416667 1
 
0.1%
-419.0034722 1
 
0.1%
-418.1909722 1
 
0.1%
-407.3840278 1
 
0.1%
ValueCountFrequency (%)
247.76875 1
0.1%
235.4923611 1
0.1%
234.5611111 1
0.1%
234.1729167 1
0.1%
232.5166667 1
0.1%
232.3027778 1
0.1%
229.0729167 1
0.1%
227.2944444 1
0.1%
224.2944444 1
0.1%
223.9888889 1
0.1%

Interactions

2024-10-16T16:17:44.944382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
182 14
 
1.7%
199 12
 
1.5%
150 12
 
1.5%
141 12
 
1.5%
175 11
 
1.3%
180 10
 
1.2%
68 9
 
1.1%
174 9
 
1.1%
45 8
 
1.0%
Other values (292) 648
78.8%
ValueCountFrequency (%)
-999 77
9.4%
0.5 1
 
0.1%
4 1
 
0.1%
35 1
 
0.1%
39 2
 
0.2%
40 3
 
0.4%
42 4
 
0.5%
43 2
 
0.2%
44 3
 
0.4%
45 8
 
1.0%
ValueCountFrequency (%)
333 1
0.1%
325.5 1
0.1%
319 1
0.1%
313 2
0.2%
302 1
0.1%
299 2
0.2%
298.5 2
0.2%
297.5 1
0.1%
292 2
0.2%
289 1
0.1%

Interactions

2024-10-16T18:50:08.753723image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.002260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:42.240610image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.904291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:44.712530image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:37.901015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:51.066418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.731681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:53.242354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.570321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:55.772373image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.553618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:58.444449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.435292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:01.015791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.266514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:43.089231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:03.086945image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:44.101817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:05.515028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:45.026136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:09.002978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.095172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:42.487877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.987887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:44.968505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:37.977529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:51.293813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.809959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:53.486916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.652865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:56.001697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.646650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:58.724112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.521553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:01.287389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.345172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:43.167951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:03.290626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:44.186723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:06.469445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:45.103436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:09.255438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.178685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:42.868737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:37.067402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:45.213910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.049533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:51.526753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.891443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:53.755498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.729826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:56.229502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.739152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:58.978868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.595622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:01.506190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.424427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:43.240958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:03.492013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:44.261849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:06.937515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:45.173802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:09.503625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.257689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:37.140939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:43.124797image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T18:49:45.418459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.120580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:51.726779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.960762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:53.983171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.805517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:56.457916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.811773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:59.187772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.668681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:01.705634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.493965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:43.483093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:03.684995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:44.338156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:07.175281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:45.246738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:09.810761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.337512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:43.351084image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:37.214454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:45.667858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.189096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:51.927086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.037788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:54.202704image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.875051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:56.658947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.892799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:59.448290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.740697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:01.916969image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.568432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:43.556992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:03.889396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:44.411173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:07.392087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:45.322072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:10.071705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.413054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:43.570796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:37.291231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:45.889259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.262636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:52.119287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.112041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:54.497417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.947157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:56.855486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.970971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:59.668609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.810932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:02.115581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.641612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:43.632529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:04.119376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:44.488599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:07.608456image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:45.404505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:10.284837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.501617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:37.368313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:38.341184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:39.193555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:40.161147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:41.049984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:41.890160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:42.721669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:43.711053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:44.570110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:45.487019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:36.582697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:37.571166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:43.804510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T18:49:50.175272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.412139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:52.364002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.266072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:54.862815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.236657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:57.130448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.125047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:59.860364image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.960181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:02.337375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.792883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:04.542128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:43.786687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:44.644262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:07.840310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:45.565982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:10.472130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.660722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:37.644684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:44.014937image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T18:49:50.398238image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.490427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:52.584889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.341641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:55.084087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.310907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:57.500872image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.203576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:00.037445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.045701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:02.528419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.860739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:43.860422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:04.859319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:44.719306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:08.057354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:45.655352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:10.671850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.744993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:44.241228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:37.741371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:50.604146image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.568625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:52.811285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.418752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:55.303830image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.389522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:57.783076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.278082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:00.215496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.119602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:02.717391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.936107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:43.956995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:05.100460image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:44.792098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:08.282483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:45.741011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:10.873312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:36.824026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:44.486018image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:37.817894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:50.852615image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:38.658570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:53.047488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:39.493290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:55.549369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:40.473864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:49:58.021152image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:41.356763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:00.423158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:42.191177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:02.915067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:43.011805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T16:17:44.026742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:05.303624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T16:17:44.869429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-16T18:50:08.512568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-10-16T16:17:45.865288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-10-16T18:50:11.150988image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T16:17:46.055797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T18:50:11.551114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

aniomesdiapm25codigoserialdia_semanaestacionfestivotemperaturahumedadpresionp1velocidad_promvelocidad_maxdireccion_promdireccion_max
020241129.12500028LunesEstacion Itagui119.76848674.873653-2.081250.000076-0.2171670.594722117.155556134.637500
120241211.83333328MartesEstacion Itagui021.91993178.140549-0.693750.0000001.4778752.436250100.706250114.094444
220241312.70833328MiercolesEstacion Itagui022.54452174.0494930.000000.0000002.0179242.903611127.286806144.834028
320241420.75000028JuevesEstacion Itagui022.35929975.3441600.000000.0000071.8774512.795625138.467361153.795833
420241520.08333328ViernesEstacion Itagui021.78091074.7365140.000000.0000141.9631812.901944149.109722166.622222
5202416-1235.12500028SabadoEstacion Itagui018.71347268.578625-3.468750.000000-1.375639-0.385625147.477083157.075000
620241710.62500028DomingoEstacion Itagui022.62871571.298049-0.693750.0000001.9901533.16652897.115972111.592361
720241818.29166728LunesEstacion Itagui122.21519472.088097-0.693750.0000281.3822152.300486147.705556158.718056
820241922.16666728MartesEstacion Itagui022.09151474.693458-1.387500.0000000.5329171.480139147.495833161.832639
9202411023.16666728MiercolesEstacion Itagui023.43133377.2249440.000000.0000282.1178473.10284783.515278104.179861
aniomesdiapm25codigoserialdia_semanaestacionfestivotemperaturahumedadpresionp1velocidad_promvelocidad_maxdireccion_promdireccion_max
264202492120.30578386SabadoEstacion Aranjuez022.97694466.242014850.5970830.0000491.2584722.327569192.377083190.800000
265202492213.84702386DomingoEstacion Aranjuez021.65270874.050972851.4021530.0073191.1286112.042708206.718056206.354167
266202492313.95615286LunesEstacion Aranjuez021.98701471.432361851.8684720.0045421.3600002.561458155.908333164.988889
267202492441671.96745886MartesEstacion Aranjuez023.75986166.039097851.3038890.0020831.5747922.940278131.738194140.452083
2682024925-1233.86473086MiercolesEstacion Aranjuez022.94472269.628611851.8152780.0007361.3101392.432083179.700000176.742361
269202492613.60900186JuevesEstacion Aranjuez021.50506970.972986852.7650000.0020631.1545142.105278217.955556207.927083
270202492727.53918486ViernesEstacion Aranjuez020.99034774.835347852.2779860.0314930.8182641.513958221.430556218.652778
271202492825.95559986SabadoEstacion Aranjuez020.36388976.159931852.5911110.0048611.0665971.977639185.232639186.740972
2722024929-402.40054886DomingoEstacion Aranjuez019.84722281.325625853.1295140.0076460.7761811.437847216.145139209.574306
273202493019.18637886LunesEstacion Aranjuez020.61680678.270903852.7865970.0004240.9647921.787986193.744444192.262500

Report generated by YData.